df84c69676
* Docker image for test and experiment Keras - Docker image with CUDA support on ubuntu 14.04 - nvidia-docker script to forward the GPU to the container - MakeFile to simplify docker commands for build, run, test, ..etc - Add useful tools like jupyter notebook, ipdb, sklearn for experiments * update nvidia-docker plugin * use .theanorc in Dockerfile * Add tensorflow to the docker image * update Docker image to cuDNN v5 * test fixes * move docker to sub directory * README for docker * Fix typos * Add visualization to Dockerfile
59 lines
1.7 KiB
Markdown
59 lines
1.7 KiB
Markdown
# Using Keras via Docker
|
|
|
|
This directory contains `Dockerfile` to make it easy to get up and running with
|
|
Keras via [Docker](http://www.docker.com/).
|
|
|
|
## Installing Docker
|
|
|
|
General installation instructions are
|
|
[on the Docker site](https://docs.docker.com/installation/), but we give some
|
|
quick links here:
|
|
|
|
* [OSX](https://docs.docker.com/installation/mac/): [docker toolbox](https://www.docker.com/toolbox)
|
|
* [ubuntu](https://docs.docker.com/installation/ubuntulinux/)
|
|
|
|
## Running the container
|
|
|
|
We are using `Makefile` to simplify docker commands within make commands.
|
|
|
|
Build the container and start a jupyter notebook
|
|
|
|
$ make notebook
|
|
|
|
Build the container and start an iPython shell
|
|
|
|
$ make ipython
|
|
|
|
Build the container and start a bash
|
|
|
|
$ make bash
|
|
|
|
For GPU support install NVidia drivers (ideally latest) and
|
|
[nvidia-docker](https://github.com/NVIDIA/nvidia-docker). Run using
|
|
|
|
$ make notebook GPU=0 # or [ipython, bash]
|
|
|
|
Switch between Theano and TensorFlow
|
|
|
|
$ make notebook BACKEND=theano
|
|
$ make notebook BACKEND=tensorflow
|
|
|
|
Mount a volume for external data sets
|
|
|
|
$ make DATA=~/mydata
|
|
|
|
Prints all make tasks
|
|
|
|
$ make help
|
|
|
|
You can change Theano parameters by editing `/docker/theanorc`.
|
|
|
|
|
|
Note: If you would have a problem running nvidia-docker you may try the old way
|
|
we have used. But it is not recommended. If you find a bug in the nvidia-docker report
|
|
it there please and try using the nvidia-docker as described above.
|
|
|
|
$ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
|
|
$ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
|
|
$ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu
|